An Experimental Investigation of Document Vector Computation Methods for Sentiment Analysis of Turkish and English Reviews

Yıl 2016, Cilt: 31 Sayı: 2, 464 - 482, 15.12.2016

Furkan Gözükara , Selma Ayşe Özel

https://doi.org/10.21605/cukurovaummfd.310341

Cited By: 2

Öz

Sentiment analysis is the task of identifying overall attitude of the given text documents by using text analysis and natural language processing techniques. In this study, we present experimental results of sentiment analysis on movie and product reviews datasets that are in Turkish and English languages by using a Support Vector Machine (SVM) classifier. Moreover, we compare different document vector computation techniques and show their effects on the sentiment analysis. We empirically evaluate SVM types, kernel types, weighting schemes such as TF or TF*IDF, TF variances, IDF variances, tokenization methods, feature selection systems, text preprocessing techniques and vector normalizations. We have obtained 91.33% accuracy as the best on our collected Turkish product reviews dataset by using C-SVC SVM type with linear kernel, log normalization TF* probabilistic IDF weighting scheme, L2 vector normalization, Chi-square feature selection, and unigram word tokenization. A very detailed comparison of the document vector computation methods over Turkish and English datasets are also presented.

Anahtar Kelimeler

Sentiment analysis, Classification, Data mining, Product reviews, Support vector machines

Türkçe ve İngilizce Yorumların Duygu Analizinde Doküman Vektörü Hesaplama Yöntemleri için Bir Deneysel İnceleme

Öz

Duygu analizi, verilen bir metin belgesinin genel yargısını, metin analizi ve doğal dil işleme teknikleri kullanarak belirleme işlemidir. Bu çalışmada, İngilizce ve Türkçe dillerinde yazılmış film ve ürün yorumlarının, Destek Vektör Makineleri (DVM) sınıflayıcısı kullanarak yapılan, duygu analizi deney sonuçları yer almaktadır. Bunun yanında, farklı doküman vektör hesaplama yöntemleri karşılaştırılmakta ve bu tekniklerin duygu analizi üzerindeki etkileri gösterilmektedir. DVM türleri, kernel çeşitleri, TF veya TF*IDF gibi ağırlıklandırma yöntemleri, TF türleri, IDF türleri, öznitelik oluşturma yöntemleri, öznitelik seçme sistemleri, metin önişleme teknikleri ve vektör normalizasyon teknikleri deneysel olarak analiz edilmektedir. Oluşturduğumuz Türkçe ürün yorumları veri kümesi üzerinde, doğrusal kernel ile kullanılan C-SVC DVM türü, log normalleştirme TF* olasılıklı IDF ağırlıklandırma yöntemi, L2 vektör normalizasyonu, Ki-kare öznitelik seçme ve tekli kelime öznitelikleri kullanılarak %91.33 doğruluk ile en iyi sonuç elde edilmiştir. Ayrıca doküman vektörü hesaplama yöntemlerinin Türkçe ve İngilizce veri kümeleri üzerindeki detaylı karşılaştırmaları da çalışmada yer almaktadır.

Anahtar Kelimeler

Duygu analizi, Sınıflandırma, Veri madenciliği, Ürün yorumları, Destek vektör makineleri

Kaynakça

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